FDG-PET/CT characterization of adrenal nodules: diagnostic accuracy and interreader agreement using quantitative and qualitative methods.

PURPOSE: To determine interreader agreement and diagnostic accuracy across varying levels of reader experience using qualitative and quantitative methods of evaluating adrenal nodules using ((18)F)-fluorodeoxyglucose-positron emission tomography/computed tomography. METHODS: 132 adrenal nodules (96 adenomas, 36 metastases) were retrospectively identified in 105 patients (49 men and 56 women, mean age 66 years, age range 45-85 years) with a history of lung cancer who underwent ((18)F)-fluorodeoxyglucose-positron emission tomography/computed tomography. For each nodule, three readers independently performed one qualitative and two quantitative measurements: visual assessment, standardized uptake value (SUVmax), and standard uptake ratio (SUVratio). Interreader agreement was calculated using percent agreement with κ statistic for qualitative analysis and intraclass correlation coefficient (ICC) for quantitative analysis. Accuracy, sensitivity, and specificity for distinguishing benign from malignant adrenal nodules were calculated for each method. RESULTS: Percent agreement between readers for visual (qualitative) assessment was 92% to 96% and κ statistic was 0.79 to 0.90 (95% confidence limits 0.66-0.99). ICC for SUVmax was 92% to 99% (95% CL 0.8-1.0), and ICC for SUVratio was 89% to 99% (95% CL 0.74-0.99). For diagnosis of malignancy, mean sensitivity and specificity for visual assessment were 80% and 97%, respectively. Mean sensitivity and specificity for SUVmax were 91% and 81%, respectively; for SUVratio, 90% and 80%. Mean diagnostic accuracy was 93%, 83%, and 84% for visual assessment, SUVmax, and SUVratio, respectively. CONCLUSION: Excellent interreader agreement is seen for quantitative and qualitative methods of distinguishing benign from malignant adrenal nodules. Qualitative analysis demonstrated higher accuracy but lower sensitivity compared with quantitative analysis.

Duke Scholars

Author Chad Michael Miller Radiology, Abdominal Imaging

Author Daniele Marin Radiology, Abdominal Imaging

Author Sandra Sue Stinnett Biostatistics & Bioinformatics, Division of Biostatistics

Author Terence Z. Wong Radiology, Nuclear Medicine

Author Erik Karl Paulson Radiology, Abdominal Imaging

Author Lisa Mei-ling Ho Radiology, Abdominal Imaging